WLAN Throughput Prediction

ABSTRACT

According to one aspect of the present disclosure, a method is implemented by a network node in relation to a basic service set (BSS) that includes an access point providing a Wireless Local Area Network (WLAN). The network node predicts a downlink data rate for downlink transmissions from the access point to a particular UE based on either signal qualities of a first set of uplink transmissions which are sent from the particular UE to the access point or a default signal quality value. The UE is either already part of the BSS or is being evaluated for admission to the BSS. The predicted downlink data rate is weighted by a metric that accounts for channel conditions of the BSS to determine a predicted downlink throughput for downlink transmissions from the access point to the particular UE. The predicted downlink throughput is used for controlling the BSS.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/943,077, filed on Feb. 21, 2014, the disclosure of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to Wireless Local Area Networks (WLANs),and more particularly to throughput prediction for WLANs.

BACKGROUND

When a WLAN Radio Access Network (RAN) wishes to make mobility decisionsfor User Equipment (UE) such as smart phones that have both WLAN RadioAccess Technology (RAT) and cellular RAT interfaces, the WLAN RAN needsa prediction of performance for an individual UE in the WLAN RAN.Previous methods for predicting the throughput of an individual UE havebeen based on only a subset of channel impairments, and can thereforeincur significant prediction error due to variations in impairments thatare not included in the predictor.

In the prior art, the use of a mathematical function, R(q), that mapsthe received signal strength indicator (RSSI) to a predictedinstantaneous data rate in an idealized channel is known. This isdiscussed by J. Pavon and S. Choi in the article “Link AdaptationStrategy for IEEE 802.11 WLAN via Received Signal Strength Measurement,”published by the IEEE in May 2013. However, this model does not takeinto account a variety of channel impairments.

SUMMARY

According to one aspect of the present disclosure, a method isimplemented by a network node in relation to a basic service set (BSS)that includes an access point providing a Wireless Local Area Network(WLAN). The network node predicts a downlink (DL) data rate for DLtransmissions from the access point to a particular UE based on eithersignal qualities of a first set of uplink transmissions which are sentfrom the particular UE to the access point, or a default signal qualityvalue. The particular UE is either already part of the BSS or is beingevaluated for admission to the BSS. The predicted DL data rate isweighted by a metric that accounts for channel conditions of the BSS todetermine a predicted DL throughput for DL transmissions from the accesspoint to the particular UE. The network node uses the predicted DLthroughput for controlling the BSS.

In one or more embodiments, the metric that accounts for channelconditions of the BSS is based on an average UE DL throughput of DLtransmissions from the access point to a plurality of respective UEs inthe BSS. In some such embodiments, the method includes calculating theaverage UE DL throughput among the plurality of UEs in the BSS based on:

-   -   a duration of each of a plurality of DL bursts transmitted from        the access point to the plurality of UEs, and    -   a quantity of bits transmitted in each of the plurality of DL        bursts.

In one or more embodiments, the weighting of the predicted DL data rateby the metric that accounts for channel conditions of the BSS isperformed as a function of an additional DL data rate which is based onsignal qualities of a second set of uplink transmissions which are sentfrom a plurality of UEs in the BSS to the access point.

In one or more embodiments, the weighting of the predicted DL data rateby the metric that accounts for channel conditions of the BSS isperformed as a function of an expected DL bit duration which is based ona signal quality of a second set of uplink transmissions which are sentfrom a plurality of UEs in the BSS to the access point.

According to another aspect of the present disclosure, a network node isdisclosed which includes a communication interface and one or moreprocessing circuits communicatively connected to the communicationinterface. The one or more processing circuits are configured to predicta DL data rate for DL transmissions from an access point to a particularUE based on either signal qualities of a first set of uplinktransmissions which are sent from the particular UE to the access pointor a default signal quality value. The access point provides a WirelessLocal Area Network (WLAN), and is part of a basic service set (BSS). Theparticular UE is either already part of the BSS or is being evaluatedfor admission to the BSS. The one or more processing circuits arefurther configured to weight the predicted DL data rate by a metric thataccounts for channel conditions of the BSS to determine a predicted DLthroughput for DL transmissions from the access point to the particularUE, and to use the predicted DL throughput to control the BSS.

In one or more embodiments, the metric that accounts for channelconditions of the BSS is based on an average UE DL throughput of DLtransmissions from the access point to a plurality of respective UEs inthe BSS. In some such embodiments, the one or more processing circuitsare configured to calculate the average UE DL throughput among theplurality of UEs in the BSS based on:

-   -   a duration of each of a plurality of DL bursts transmitted from        the access point to the plurality of UEs, and    -   a quantity of bits transmitted in each of the plurality of DL        bursts.

In one or more embodiments, the one or more processing circuits areconfigured to weight the predicted DL data rate by the metric thataccounts for channel conditions of the BSS as a function of anadditional DL data rate for the particular UE which is based on a signalquality of a second set of uplink transmissions which are sent from aplurality of UEs in the BSS to the access point.

In one or more embodiments, the one or more processing circuits areconfigured to weight the predicted DL data rate by the metric thataccounts for channel conditions of the BSS as a function of an expectedDL bit duration for the particular UE which is based on a signal qualityof a second set of uplink transmissions which are sent from a pluralityof UEs in the BSS to the access point.

Of course, the present disclosure is not limited to the above featuresand advantages. Indeed, those skilled in the art will recognizeadditional features and advantages upon reading the following detaileddescription, and upon viewing the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network including a plurality of accesspoints (APs) and user equipment (UEs).

FIG. 2 illustrates example UE locations at which DL throughputpredictions could be useful.

FIG. 3 illustrates an example histogram of signal quality values.

FIG. 4 illustrates an example data rate function, R(q).

FIGS. 5A-C illustrate a plurality of possible RSSI probability densityfunctions, p(q).

FIG. 6 illustrates an example weighted rate function.

FIG. 7 illustrates an example window function to cut off low data rates

FIG. 8 is a flowchart illustrating an example method of predicting UE DLthroughput in a BSS.

FIG. 9 is a flowchart illustrating another example method of predictingUE DL throughput in a BSS.

FIG. 10 illustrates a method that incorporates aspects of FIGS. 8-9.

FIG. 11 is a flowchart illustrating an example implementation of block102 of FIGS. 8-9.

FIG. 12 illustrates an example network node.

DETAILED DESCRIPTION

Aspects of the present disclosure provide novel techniques forpredicting DL throughput in a WLAN, and for applying that predicted DLthroughput to controlling a basic service set (BSS) of the WLAN. Thesetechniques are novel in that, rather than attempting to predict DLthroughput based on only one or two aspects of performance (such as RSSIor channel utilization), they incorporate statistical measurements ofrecent downstream transmissions in the WLAN to improve the prediction.The statistical measurements provide insight into the achievable DLthroughput taking into account all channel impairments, without havingto separately measure each aspect of impairment.

The term “basic service set” refers to a set of user equipment (UEs) andthe wireless access point (AP) to which they connect in a 802.11Wireless Local Area Network (WLAN). FIG. 1 illustrates an examplecommunication network 20 that includes a plurality of WLAN APs 22, eachof which has their own respective BSS 24 that supports one or more UEs26. In some embodiments, the UEs 26 may also be configured tocommunicate via non-WLAN networks (e.g., 3rd Generation Partnership“3GPP” cellular networks). Of course, although only three UEs are shownin the BSS 24A of FIG. 1, it is understood that a given BSS couldinclude any number of UEs.

According to one aspect of the present disclosure, a DL data rate ispredicted for DL transmissions from an AP 22 to a particular UE 26 basedon either signal qualities of a first set of uplink transmissions whichare sent from the particular UE 26 to the access point 22, or based on adefault signal quality value. The predicted DL data rate is weighted bya metric that accounts for channel conditions of the BSS 24 to determinea predicted DL throughput for DL transmissions from the access point 22to the particular UE 26 that is either part of the BSS 24 or is beingevaluated for admission to the BSS 24. The predicted DL throughput isthen used for controlling the BSS 24. This improves upon prior artthroughput prediction techniques because a wide variety of channelimpairments are accounted for.

According to one aspect of the present disclosure, recent statisticalmeasurements of UE DL throughput are incorporated into predictions. Thismodifies the known R(q) prediction by using additional terms, such as Tb(which represents a new method for measuring average UE DL throughput),Db′ (which represents a bit duration), and Rb (which represents a datarate). The DL throughput prediction methods depicted in FIGS. 8-10 beloware able to account for a wide variety of types of channel impairmentswithout having to estimate each of the impairments individually.

Referring again to FIG. 1, the various WLAN APs 22 provide connectivityto a wide area network 28 (e.g., the Internet). In some embodiments, aWLAN control node 30 is present and controls operation of the variousWLAN APs 22 and their BSSs 24. The UEs 26 may be a cellular telephone,smartphone, personal digital assistant (PDA), tablet computer, laptopcomputer, laptop embedded equipment (LEE), laptop mounted equipment(LME), USB dongle, or any other device equipped with 802.11 (i.e.,Wi-Fi) based wireless communication capabilities. Some UEs may also haveradios for connecting to non-WLAN radio access networks (RANs), such as3GPP RANs, 3GPP2 RANs, or WiMAX RANs.

The predicted DL throughput for a particular UE may be used for avariety of purposes, such as determining whether to admit or deny theparticular UE or selected ones of its traffic flows to the BSS. It maybe desirable, for example, to admit data flows but deny voice flows of agiven UE, (e.g., if QoS requirements of the voice flow could not beaccommodated). Such traffic steering could be based on QoS requirementsof the particular UE and/or QoS requirements of its particular trafficflows.

FIG. 2 illustrates some example UE locations within the coverage area ofan example BSS 24 at which DL throughput predictions could be useful todecide when to switch service from the 3GPP RAN of cell 32 to the BSS24, or vice versa, based on comparison of the predicted DL throughputfrom each network. At location “A”, the UE is in an area of cellularcoverage and moving into the edge of coverage for the BSS 24. As the UEmoves far enough into the BSS 24, the DL throughput prediction from theBSS 24 should at some point exceed the expected DL throughput for thecell 32, at which time handover to the BSS 24 would likely be desirable.At location “B”, the UE is moving to an area of lower predicted DLthroughput from the BSS 24. At some point, the BSS 24 DL throughputprediction will be lower than the predicted DL throughput from the cell,at which time handover to the cell 32 would likely be desirable. The DLthroughput prediction techniques discussed above could be used todetermine how to handle the presence of UE 26A in the BSS 24, andoptionally also the departure of UE 26B from the BSS 24. It should benoted that these comparisons of predicted DL throughput could be appliedfor handover decisions between any WLAN BSS and one or more overlappingcells of any RAN, such as another WLAN BSS within the same RAN, anotherWLAN BSS in a different RAN, a cell in a 3GPP RAN, a cell within a 3GPP2RAN, or a cell within a WiMAX RAN.

In addition to admission control, the DL throughput prediction couldalso be used for traffic overload control by deciding whether to releasethe particular UE or selected ones of its flows from the BSS 24 based onthe DL throughput prediction. The DL throughput prediction could also beused for mobility control (e.g., load balancing), and performancemonitoring of the network (e.g., for longer term changes that areimplemented after the particular UE is no longer connected and/or nolonger trying connect to the BSS). Specific details of a variety of DLthroughput prediction methods will now be discussed in detail.

Overview of WLAN Performance

The measure of UE performance will be a prediction of the downlink (DL)throughput of unicast data frames, i.e., the user throughput from the802.11 access point (AP) 22 to the particular UE 26. Uplink (UL)throughput will be ignored. In IEEE 802.11 terminology, UL is alsoreferred to as “upstream” (US), and DL as “downstream (DS), however, ULand DL will be used in the discussion below. This simplification of UEperformance being a prediction of DL throughput of unicast data framesis made to allow a simpler comparison with other RATs, and because themajority of user traffic often occurs in the DL direction. The DLthroughput in a WLAN can be affected by many factors, such as:

-   -   Transmit power, antenna gain, path loss, and fading due to        multipath and Doppler shifts (together, these determine the        signal-to-noise ratio (SNR) at the receiver);    -   Contention with other WLAN stations on the same frequency        channel (co-channel). These contending stations may be in the        same BSS, i.e., served by the same AP, or in a nearby BSS, i.e.,        served by a different AP, or in an independent BSS, i.e., direct        communication between stations without an AP;    -   Contention from other WLAN stations that are operating on a        different but overlapping frequency channel (adjacent channel        interference);    -   Non WLAN interference sources, such as microwave ovens,        Bluetooth devices, and cordless phones that do not observe the        same collision avoidance mechanisms as in 802.11.

Co-channel contention can affect throughput in two main ways dependingon whether the transmitting station (STA) (i.e., UE or AP) senses thetransmission from the contending station. As used herein, STA refers toeither an 802.11 UE or an 802.11 AP. The two ways in which co-channelcontention can affect throughput are:

-   -   Transmission deferral: If the transmitting 802.11 station (STA)        hears the contending STA, the transmitting STA will defer its        transmission until it senses that the channel is clear. When the        channel is clear, the transmitting STA uses a randomly chosen        back-off time to further delay its transmission attempt, in        order to reduce the probability of colliding with other        contending STAs. Transmission deferral reduces throughput by        reducing the amount of time that the transmitting STA can access        the channel.    -   Collisions: If the transmitting STA does not hear the contending        STA, the transmitting STA may attempt to transmit at the same        time as the contending STA. The resulting collision causes a        decrease in the signal-to-interference plus noise ratio (SINR)        at the receiving STA for some or all of the transmission time.        This reduction in SINR reduces the instantaneous data rate that        can be transmitted successfully, and often causes a frame error,        since the transmitting STA is not aware of the collision a        priori.

A co-channel collision may also occur when a contending STA is not ableto properly decode the network allocation vector (NAV) that reserves thechannel for an ongoing transmission and its ACK. As a result, the STAmay think that the channel is clear prematurely and attempt atransmission that collides.

Adjacent channel interference and other noise sources impact throughputin similar ways. If the interference is bursty, it will interfere withsome of a STA's transmissions, thereby decreasing the SINR of thosetransmissions. The resulting increase in frame error rate may cause therate adaptation algorithm to select lower data rates.

If the interference is persistent, the decrease in SINR will alsopersist, and the rate adaptation algorithm may persistently select lowerdata rates. In addition, when the transmitting STA senses that the noisefloor is higher, it may reduce its receiver sensitivity. As a result,the STA will not be able to receive ACKs from other STAs if the receivedsignal strength is not high enough to be received by the de-sensitizedreceiver.

Traffic Steering

Network based traffic steering is envisaged to occur at two points intime:

-   -   WLAN entry: When the UE attempts to enter a WLAN, it will be in        one of two conditions:        -   BSS edge: If the UE moved into the BSS coverage area from            outside that area, and it did not have service from another            RAT, the UE will attempt to enter the WLAN at the edge of            the coverage area. The signal strength will be low at that            location, and therefore the achievable data rate will be            low.        -   Anywhere in the BSS: This case occurs if the UE was            previously being serviced by another RAT (either Idle or            Connected depending on whether data was ready for            transmission), and the signal quality from that RAT dropped            low enough to trigger the UE to search for an alternate RAT            such as WLAN. Alternatively, the UE may have just enabled            the WLAN interface, e.g., due to power-on, or due to the            user enabling the data service. In this case, the UE may be            anywhere in the coverage area, so the signal strength would            correspond to the location relative to the AP.

If the UE attempts entry at a location with low signal strength, or theBSS performance is reduced due to contention or interference, thenetwork could attempt to defer admission into the WLAN until thepredicted performance in the WLAN is deemed to be better than thequality of the overlapping RATs.

-   -   WLAN departure: After admission to the BSS, the UE may move        around within the coverage area to a location with poorer link        quality, or the loading of the BSS may increase leaving fewer        resources for the UE. Performance predictions are needed in        these situations to allow the network to steer the UE to a RAT        that can offer better UE performance.

It may be desirable to deny admission to a BSS in a way that causes a UEto defer its access attempt. Ideally, the network should be able todefer the UE in a way that causes it to re-attempt access after a longenough pause that the path loss or channel loading has had a chance tochange significantly, e.g., 5 seconds. These periodic transmissionswould allow the network to monitor changes in the UE's WLAN linkquality, so that the UE can be admitted when the quality is high enough.

In order to compare predicted performance between RATs, the networkneeds to establish a mapping between the UE identifiers used in eachRAT. In WLAN, the identity of a transmitting STA is indicated by thesource medium access control (MAC) address in the 802.11 frame. To mapthis identity to the other RATs, the network needs to determine amapping from the MAC address to the UE's identity within 3GPP, e.g., theinternational mobile station identifier (IMSI). For traffic steering inWideband Code Division Multiple Access (WCDMA) networks, for example,the network could learn the IMSI from the Authentication, Authorization,and Accounting (AAA) server during the extensible authenticationprotocol (EAP) authentication phase. During this phase, the UE exchangesEAP messages over the 802.11 radio link, and correspondingly, the APexchanges EAP messages with an AAA server, e.g., using remoteauthentication dial-in user service (RADIUS) protocol.

Performance Predictions

Several aspects of WLAN performance could potentially be predicted by anAP 22 for the purpose of controlling the BSS 24 (e.g., for trafficsteering). Two possible types of predictions include:

-   -   UE specific predictions, which predict the instantaneous        throughput of a specific UE; and    -   BSS wide predictions, which predict the UE throughput averaged        over all UEs in the WLAN cell (i.e., the BSS).

UE specific predictions are useful for mobility decisions that attemptto optimize the performance of the individual UE, since they allow themobility decision to be tailored to the UE's current channel condition.In this regard, the term “mobility” refers to a change in the primarypacket service path from one RAT to another. It does not imply that theUE can report measurements of a target RAT to a source RAT, as isusually the case with 3GPP handovers. In contrast, BSS wide predictionsare more useful for load balancing decisions, where we wish to balancethe “average” UE experience between RATs that overlap, regardless of anindividual UE's experience.

One basis for selecting a best RAT could be a comparison of UE specificthroughput predictions in each RAT, e.g.:

RAT=arg max{R _(i)(SINR_(i))×(1−util_(i))}  equation (1)

where:

R_(i), SINR_(i), and util_(i) are the data rate, SINR, and totalresource utilization in RAT i.

The following is a short list of potential UE-specific measurements thatcould be made by an AP 22 to predict DL throughput:

-   -   UL RSSI        -   For some chipsets, such as the QUALCOMM ATHEROS chipset in            ERICSSON APs (and also likely in other chipsets), the RSSI            is actually an estimate of the received signal strength            relative to an estimated noise floor power. So, in some            embodiments, the RSSI is an estimate of SNR, rather than an            estimate of the absolute signal strength. The reported RSSI            in such embodiments is equal to:            fixed_noise_offset+SNR_estimate, where the            fixed_noise_offset is typically −95 dBm. In other            embodiments, the measured RSSI value could be an estimate of            absolute signal strength.    -   DL user plane throughput of recent transmissions        -   This would involve measuring DL throughput on a per UE            basis.    -   DL frame error rate statistics per transmission rate.        -   In ERICSSON APs, for example, the Minstrel rate adaptation            algorithm that is used maintains statistics about the            probability of frame transmission error for each of the            transmission rates, based on previous attempts to use those            rates, and periodic polling of other rates.

The following is a short list of potential BSS wide measurements thatcould be made by an AP to predict DL throughput:

-   -   UE DL throughput averaged over all UEs    -   Channel availability        -   This is a measure of the percentage of time that the channel            is not busy.        -   The busy times could be further categorized into:            -   AP transmission time, including MAC layer ACKs            -   AP reception time for frames addressed to the AP            -   Busy time for 802.11 frames outside the BSS, i.e., where                the AP is neither source nor destination            -   Busy time for non 802.11 energy, which includes 802.11                transmissions that could not be decoded, e.g.,                transmissions on overlapping channels, or where the SINR                was insufficient to decode the 802.11 header properly.    -   AP transmission efficiency, which is defined as the ratio of        successful transmissions to total transmissions on the basis of        transmission time, i.e., this is a measurement of transmission        error time duration, rather than packet error rate.    -   Noise floor        -   This is a measure of the background noise level when no            bursty transmission sources are present on the channel.    -   “Phy error” rate        -   In WLAN, a “phy error” refers to a false detection of a            frame preamble. A phy error causes the transmitter to            erroneously think that the channel is busy.    -   Modulation and coding scheme (MCS) statistics showing the        distribution of Tx and Rx rates that were used in the BSS

UE Association States

Performance predictions are needed while the UE is in different statesof association to the BSS:

-   -   Disassociated and attempting to associate        -   This state exists either before the UE 26 has associated            with the BSS 24 for the first time, or after the network has            commanded the UE 26 to disassociate from the BSS 24. When            the UE is attempting to associate, it will transmit one or            more management frames to the AP (e.g., probe request,            authentication request, or association request) that allow            the AP to know that the UE is in the coverage area. The AP            can use the RSSI of these received frames to predict the            instantaneous DL throughput that the UE might get in the            WLAN. Scenarios:            -   UE connected in a 3GPP RAT moves into WLAN coverage                -   There may exist different triggers within the UE                    that cause the UE to search for WLAN service. For                    example, the UE may perform a periodic search for                    WLAN coverage even while in good 3GPP signal                    conditions. As another example, the trigger may be                    conditioned on the UE having data ready to send. The                    trigger will determine the likelihood of having a UE                    initiate a WLAN association as soon as BSS coverage                    is available, i.e., at the edge of the BSS, versus                    locations that are more distributed throughout the                    BSS area. If it is always at the cell edge, then it                    is even more valuable to find a way to defer the                    UE's entry until it has a better WLAN signal                    quality.            -   UE idle in 3GPP moves into WLAN coverage                -   In this case, the UE would be monitoring 3GPP signal                    quality (as part of idle state channel selection),                    but the 3GPP network would not have an instantaneous                    measure of signal quality. As in the previous                    scenario, it could be useful to know whether the UE                    always searches periodically for WLAN coverage, or                    only when it has data to send.    -   Associated and active        -   The associated state exists after the UE has associated with            the BSS. If the UE recently exchanged traffic in the BSS            (i.e., it was active), the recent traffic history could be            used to predict the instantaneous DL throughput of the UE.    -   Associated and inactive        -   The AP does not distinguish this state from the previous            state (associated and active). A UE may remain associated            for a long time without exchanging traffic, since the            inactivity timeout for removing associations is usually very            long, e.g., on the order of hours rather than seconds.            Therefore, measurements of UE specific DL throughput could            be very stale and inaccurate. Note that the WLAN does not            use the concept of a radio resource control (RRC) Connection            that is used in 3GPP RATs, since there are no physical radio            resources dedicated to an associated STA. Options to deal            with this inactive substate are:            -   Use the latest individual UE DL throughput measurement,                even though it is stale.            -   Revert to using the BSS wide average UE DL throughput                measurement.                Although management frames are specifically discussed                above, it is understood that other types of frames                (e.g., control frames and/or data frames) also indicate                that the UE is in a coverage area, and it is understood                that an AP could use the RSSI of such frames in addition                to, or as an alternative to, the RSSI of the management                frames to predict UE DL throughput.

Average UE DL Throughput Measurements

One option for predicting average UE DL throughput (averaged over allUEs in the BSS) is described below. During each sampling interval, n,the AP 22 calculates an instantaneous DL throughput per UE as:

T(n)=B(n)/U(n)/(sampling_period)  equation (2)

where:

-   -   B(n) is the number of traffic bits transferred successfully on        the DL during sampling interval n,    -   U(n) is the number of STAs (UEs) to which the traffic bits were        sent during sampling interval n,    -   T(n) is the estimated instantaneous DL throughput per UE in        sampling interval n, and    -   sampling_period is the time between sampling intervals.

The sampling period, i.e., the time between successive samplingintervals, could be chosen to be small (e.g., 1 second) in order toreduce the inaccuracy introduced by assuming that all U(n) stations havedata queued for the whole sampling period.

Next, these instantaneous DL throughput samples, T(n), could be smoothedby averaging them over a longer AP reporting interval, e.g., on theorder of 60 seconds. This averaged DL throughput could be calculated byonly including sampling intervals where B(n) and U(n) are non-zero. IfB(n) and U(n) are zero for all sampling intervals, a nominal DLthroughput could be reported. The AP could report this average UE DLthroughput to the WLAN control node 30 every AP reporting interval.

The WLAN control node 30 could perform a second stage of smoothing byaveraging the reported DL throughput measurements with a rectangularsliding window over the most recent reports, e.g., the 15 most recentreports. For example, with a sampling period of 1 second, an APreporting interval of 60 seconds, and a second stage smoothing window of15 reports, the result would be an estimate of average UE DL throughputover the most recent 15 minutes.

The resulting 15 minute DL throughput estimate may be inaccurate since:

-   -   It does account for sampling intervals when traffic is queued        for a UE but no traffic could be transmitted to the UE during        the sampling interval, e.g., due to channel contention, or        queuing behind other UEs.    -   It assumes that traffic is queued for each of the active UEs for        the duration of the sampling interval. For example, a typical        100 Kbyte web page lasts only 16 ms at a transmission rate of 50        Mbps. If an AP transmits a 100 Kbyte burst in 16 ms, and then no        more data is queued for the UE for the remaining 984 ms of the        sampling interval, the estimated DL throughput for that 1 second        sampling interval would be only 0.8 Mbps, rather than the 50        Mbps that the UE actually experienced.

It may be possible to reduce the error by averaging the DL throughputonly over the portion of the sampling period when the channel was busy.So, extending the example above, if the channel is busy for 100 ms ofthe 1 second sampling period (16 ms for the AP's transmissions and 84 msdue to activity by other STAs), the AP could use the 100 ms busy periodto calculate the DL throughput as 8 Mbps (=100 Kbytes/100 ms). Thiswould be a more accurate estimate, but still somewhat pessimistic inthat it assumes that each UE had data queued for the entire busy time.

Improved Average UE DL Throughput Estimate

It may be possible to improve the accuracy of the average UE DLthroughput estimate further, at the expense of more processing in theAP. One way to improve the estimate is to measure the duration of eachUE data burst, and the number of bits transferred in the burst. Thiscould be implemented by adding a set of timestamps and bit counters:

-   -   Per UE        -   burst duration counter, Db(u,i), where u is a UE index and i            is the burst index        -   burst bit counter, Bb(u,i)    -   Per AP        -   time duration counter, Db        -   bit counter, Bb

The operation of the estimator can be explained as follows. Assume thatall of a UE's queues are initially empty. When any one of the UE's DLqueues goes from empty to non-empty, the AP records a per-UE timestamp,Dbstart(u,i), to indicate the start of a data burst. At the same time,it initializes the UE's data burst bit counter, Bb(u,i), to zero. Whileat least one of the UE's queues still contains data, the AP incrementsthe UE's bit counter by the number of bits transmitted for eachsuccessful frame transmission. When all of the UE's queues become empty(i.e, the end of the data burst), the AP calculates the duration of thedata burst, Db(u,i), as the difference between the current time,Dbend(u,i), and the time at the start of the burst, Dbstart(u,i). The DLthroughput, Tb(u,i), experienced over data burst i is then just theratio of the number of bits transmitted during the burst, Bb(u,i), tothe duration of the data burst, Db(u,i), i.e.,

Tb(u,i)=Bb(u,i)/Db(u,i)  equation (3)

The DL throughput estimate of an individual UE, u, can be improved byaveraging the estimates over a longer observation interval, I, to get:

$\begin{matrix}{{{Tb}\left( {u,I} \right)} = \frac{\sum\limits_{i \in I}^{\;}{{Bb}\left( {u,i} \right)}}{\sum\limits_{i \in I}^{\;}{{Db}\left( {u,i} \right)}}} & {{equation}\mspace{14mu} (4)}\end{matrix}$

For estimating the DL throughput of the entire BSS, it is sufficient toaccumulate the per-UE bit counters and duration counters into the APwide counters (Bb and Tb) that sum over all UEs and all data bursts inthe BSS over interval I. Then at the end of observation interval I, anaverage UE burst DL throughput, Tb(I), over the interval can becalculated simply as:

Bb(I)=Σ_(iεI,all u) Bb(u,i)  equation (5)

Db(I)=Σ_(iεI,all u) Db(u,i)  equation (6)

Tb(I)=Bb(I)/Db(I)  equation (7)

Note that this method properly accounts for times when multiple UEs havedata queued, since the time accumulator, Db(I), sums the time that eachUE is waiting with data in its queues. As a simple example, if two UEshave data queued for an entire observation interval (e.g., 10 seconds),and the AP successfully sends 100 Mbits to each UE, the estimatedaverage UE burst DL throughput would be:

Tb(I)=(100 Mbits+100 Mbits)/(10 seconds+10 seconds)=10 Mbps

The estimate includes both queuing delay (time waiting in the queue) andscheduling delay (time waiting for media access, retransmissions, andACKs).

UE Specific DL Throughput Prediction Upon Entry into BSS

When a UE decides to look for a WLAN BSS, it exchanges a sequence ofmanagement and control frames with the AP. The management and controlframes that the UE sends (probe request, authentication request,association request, and ACK) are usually sent at a fixed, low data rate(e.g., 1 Mbps in the 2.4 Mbps band) in order to achieve a highprobability of successful transmission. Since the data rate is notadaptive at this point, it does not provide an indication of linkquality. However, the AP can measure the RSSI of the frames that itreceives from the UE and use the RSSI as an indication of link quality.Next we consider how the UL RSSI can be mapped to a prediction of the DLthroughput.

In the absence of interference and collisions, it is possible to relatethe UL RSSI to the instantaneous data rate that can be used by the UE inthe UL direction. This is possible since the RSSI that is reported bysome current WLAN chips is really an estimate of SNR, plus fixed offsetof, e.g., about −95 dBm, rather than the absolute received signalstrength. This relationship could be measured or predicted a priori, andcould look something like the function 40 shown in FIG. 3. The middlerange 42 of the chart 40 in FIG. 3 is approximately linear. In thisexample, the data rate function could be approximated by the continuousfunction:

R(q)˜=2*(q+92)  equation (8)

-   -   over the range −90<q<−62

where: q is the UL RSSI.

Alternatively, the data rate function could be approximated by any othercontinuous function, such as a polynomial equation.As yet another alternative, the function shown in chart 40 of FIG. 3could be quantized and implemented as a look up table. To create thelook up table, the independent variable, q, would be divided intodiscrete bins. For example, the histogram could use RSSI bins of size 1dBm and then quantize the measured signal quality values (e.g., RSSIvalues) into the 1 dBm bins.

The actual data rate function will depend on the fading characteristicsof the channel, but as a first approximation, the fading dependencecould be ignored. The data rate function will also depend on the numberof spatial multiplexing streams that can be transmitted. Since the rankof the channel would not be known when the UE first enters the BSS, wecould assume the use of a single stream (rank 1), since most handsetstoday are limited to single stream operation.

Most UEs have an equivalent isotropically radiated power (EIRP) that issignificantly less than the AP's EIRP. In the 2.4 GHz band, a typical UEhas an EIRP of about 14 dBm, though the UE may use a slightly lower EIRPat higher data rates in order to limit out-of-band interference. TheEIRP at the AP is configurable and is usually higher than the EIRP ofthe UE. To relate the UL RSSI to the DL RSSI, one could make thefollowing assumption about the difference in transmitted power at the APand UE:

RSSI_(DS)=(S _(DS) −S _(US))+RSSI_(US)  equation (9)

where:

-   -   S_(DS) is the transmitted power at the AP's antenna port    -   S_(US) is the transmitted power at the UE's antenna port

A typical UE has a single transmit chain with a conducted output powerof about 15 dBm, while an ERICSSON AP typically uses 3 transmit chainswith a conducted output power of about 18 dBm per chain, giving a totalpower difference of about 8 dB. Since the AP knows its transmittedpower, variations in the AP transmit power could be accounted for. Butsince the UE transmit power is not known, it could be assumed.

This difference in transmitted power has limited impact, since mostsuccessful DL transmissions require a successful ACK transmission in theUL. The 802.11 standard specifies the data rate at which an ACK is sent,as a function of the data rate used for the original frame. For example,using the single stream IEEE 802.11n rate set for a 20 MHz bandwidth,the rules for selecting an ACK data rate in response to a data framedata rate are as follows:

TABLE 1 Data frame data rate with 800 ACK control frame MCS Index nsguard interval (Mbps) data rate (Mbps) 0 6.5 6 1 13.0 12 2 19.5 12 3 2624 4 39 24 5 52 24 6 58.5 24 7 65.0 24

Since the ACK is transmitted at the UE's transmitted power, the DL datarate will be limited by the data rate required for the ACK transmissionon the UL. Using the example above with single stream IEEE 802.11n at 20MHz bandwidth, if the UE's power causes the UL data rate to be limitedto 12 Mbps, the DL data rate would be limited to 19.5 Mbps. As a firstapproximation, the DL RSSI could be assumed to be equal to the UL RSSIfor the purpose of predicting usable data rate. Alternatively, theestimated DL RSSI could be adjusted according to the current outputpower setting of the AP relative to an assumed UE transmit power.

Now, to translate the DL data rate prediction into a DL throughputprediction, contention and interference from other sources in the BSSshould be accounted for. Two possibilities are to take advantage ofmeasurements of either the channel utilization, or the average UE DLthroughput.

Weighted Channel Utilization

The channel availability in time, (1−utilization), could be used topredict the throughput available, by multiplying by the UE-specificinstantaneous data rate prediction, R(qu), where qu is a measured valueof the UL RSSI, as shown in equation (10) below. An example data ratefunction, R(q), is shown in FIG. 4.

Throughput(qu)˜=R(qu)×(1−utilization)×k  equation (10)

However, in a WLAN, it is difficult to predict the amount of channeltime that could be used by a newly arriving UE, since channel time isshared in a distributed fashion between all overlapping stations and APsin accordance with the collision avoidance algorithm.

To use this method, one could assume the percentage of the availabletime that could be used by the newly arriving UE. In the equation above,this percentage is shown as the factor k. The factor k also accounts foreffects such as retransmissions and collisions.

For example, if the predicted DL transmission rate is 26 Mbps (R(qu)=26Mbps), and the channel is estimated to be utilized 75% of the time(utilization=0.75), and one assumes that the new UE can consume half ofthe idle time (k=0.5), one can predict the DL throughput to be:

Throughput˜=26 Mbps×(1−0.75)×0.5=3.3 Mbps.

There is a significant chance for error in this method, since it assumesthat:

-   -   The new UE will defer its transmissions until the channel is        idle; statistically, data bursts to the new UE would be expected        to sometimes occur at the same time as the pre-existing traffic,        and thereby lower the perceived throughput of all stations.    -   The transmissions from other contending stations will not change        significantly; however, the amount of channel time that the new        UE can consume is dependent on how many stations contend for the        channel simultaneously. For example, if the channel is fully        occupied by one station, the new (second) UE will actually be        able to access about 50% of the channel time (assuming both        stations have traffic of the same class of service) since the        collision avoidance mechanism in 802.11 causes the 2 stations to        each transmit about the same rate of frames, i.e., the first        UE's frame rate will be cut in half, and the second UE's frame        rate will be equal to that.    -   Frame errors caused by bursty interference sources are        infrequent. If there are bursty error sources that interfere        after the AP starts transmitting, this degrades the frame error        rate, and may cause the rate adaptation algorithm to use a lower        transmission rate.

Weighted Average UE DL Throughput

Perhaps a better prediction of the DL throughput that could be realizedby a UE is given by the average UE DL throughput measured recently inthe BSS. To account for the instantaneous channel condition of the UE,one could weight this average DL throughput by a factor based on theUE's current data rate relative to the average data rate in the BSS,i.e.:

Throughput(qu)˜=R(qu)×(Tb/Rb)  equation (11)

where:

-   -   R(qu) is the predicted DL data rate for a UE “u” based on the UL        RSSI, qu, measured at the AP, i.e., this is a UE-specific DL        data rate prediction;    -   Rb is the predicted DL data rate averaged over all UEs in the        BSS, i.e., this is the average DL data rate; and    -   Tb is the average UE DL throughput of the UEs in the BSS        considering only periods when there is DL data queued to send to        a given UE.

Note that the observation interval index, I, has been dropped from thenotation for simplicity, as it is understood that the DL throughputestimates apply to a particular observation interval. Tb is measuredover the UEs that were recently active in the BSS, and thereforerepresents an average over the channel conditions at their locations inthe BSS. The average data rate over these locations, Rb, could becalculated from the R(q) data rate function as:

Rb≈Σ _(q) p(q)×R(q)  equation (12)

where

-   -   q is the measured UL RSSI, and    -   p(q) is the probability of RSSI value, q.

To calculate p(q), a histogram, P(q), of RSSI values could be collectedand normalized to get the quantized probability density function, p(q):

$\begin{matrix}{{p(q)} \approx \frac{P(q)}{\sum\limits_{q}^{\;}{P(q)}}} & {{equation}\mspace{14mu} (13)}\end{matrix}$

We can substitute equation 12 into equation 11 to get a practical way ofcalculating Rb:

$\begin{matrix}{{Rb} \approx \frac{\sum\limits_{q}^{\;}{{P(q)} \times {R(q)}}}{\sum\limits_{q}^{\;}{P(q)}}} & {{equation}\mspace{14mu} (14)}\end{matrix}$

FIGS. 5A-C illustrate a plurality of possible RSSI probability densityfunctions, p(q), and FIG. 6 illustrates an example weighted data ratefunction p(q)×R(q) in which the probability density function p(q) hasbeen applied to the function R(q). As shown in FIGS. 5A-C, p(q) could beapproximated by a uniform distribution over a range of RSSI values (FIG.5A), so that Rb just becomes a constant. Alternatively, p(q) could bedetermined a priori from an assumed cell shape, such as a hexagonal cell(FIG. 5B), and path loss geometry, along with an assumption of uniformlydistributed clients in the cell. A better method could be to measurep(q) actively at the AP by collecting a histogram, P(q), of the RSSIvalues over the same measurement period that is used to measure theaverage UE DL throughput, Tb (FIG. 5C). By measuring p(q) actively, onecan correct Tb when the distribution of UEs is not uniform in thecoverage area.

As a final enhancement, a window function, W(q), could be applied to thedata rate function, R(q), in order to remove DL throughput predictionsbelow some threshold value of RSSI. An example window function that cutsoff low data rates is shown in FIG. 7. Applying the window functionW(q), the DL throughput prediction then becomes:

Throughput(qu)˜=R(qu)×((Tb/Rb)×W(qu))  equation (15)

Prediction Using the Weighted Average Bit Duration

In the previous section (entitled “Weighted Average UE DL Throughput”),the term Rb is calculated as the expected value of the DL data rate perframe. In contrast, the DL throughput term Tb is calculated as the ratioof the number of bits transmitted to the sum of the bit durations, i.e.,the average DL throughput per bit, rather than the average data rate perframe. Thus to avoid inconsistencies between the Rb and Tb terms, abetter formulation of Rb can be based on the expected value of the bitduration, rather than the expected value of the data rate. To do this, asmall change could be made to the previous equation, as follows:

$\begin{matrix}{{Rb}^{\prime} \approx \frac{1}{\sum\limits_{q}^{\;}{{p(q)}*\left( {1/{R(q)}} \right)}}} & {{equation}\mspace{14mu} (16)}\end{matrix}$

where:

-   -   q is the RSSI, and    -   p(q) is the probability of RSSI value, q.        By again using equation 2 to substitute for p(q), we get the        more practical form:

$\begin{matrix}{{Rb}^{\prime} \approx \frac{\sum\limits_{q}^{\;}{P(q)}}{\sum\limits_{q}^{\;}{{P(q)}*\left( {1/{R(q)}} \right)}}} & {{equation}\mspace{14mu} (17)}\end{matrix}$

Or, inverting the equation, we get the expected bit duration:

$\begin{matrix}{{Db}^{\prime} = {{1/{Rb}} \approx \frac{\sum\limits_{q}^{\;}{{P(q)}*\left( {1/{R(q)}} \right)}}{\sum\limits_{q}^{\;}{P(q)}}}} & {{equation}\mspace{14mu} (18)}\end{matrix}$

In some embodiments, the term 1/R(q) could be precomputed as a look-uptable, rather than performing a division operation. The DL throughputprediction can now be written as:

Throughput(qu)˜=R(qu)×Tb×Db′  equation (19)

If the optional window function is applied, then the DL throughputprediction could be written as:

Throughput(qu)˜=R(qu)×Tb×Db′×W(q)  equation (20)

Here the division by Rb has been replaced by a simpler multiplication byDb′.

Summary of Prediction Methods

The following list summarizes the proposed DL throughput predictionmethods, in order of increasing complexity.

1. UE DL Throughput Averaged Over all UEs in the BSS, Tb

This prediction is not UE specific, but may be a fallback in the absenceof other measurements. Accuracy could be increased by using timestampsand bit counters, as discussed above.

2. UE-Specific Instantaneous Data Rate Prediction, R(Gu)

Function R(q) maps the UE specific RSSI, qu, to a predictedinstantaneous DL data rate, R(qu), ignoring contention and interference.This predictor ignores BSS loading.

3. R(qu)×(1−utilization)×k

Since channel time is shared in a distributed way between alloverlapping stations and APs, this method is not accurate. It alsoignores the impact of noise below the clear channel assessmentthreshold, i.e., noise that does not show up as channel utilization.

4. R(qu)×(Tb/Rb) (See FIGS. 8-9, and Equation (11) Above)

In this method, the utilization is replaced by the UE average DLthroughput measurement, Tb, and is normalized by the average data ratein the BSS, Rb.

5. R(qu)×((Tb/Rb)×W(qu)) (See FIGS. 8-9, and Equation (15) Above)

This extends the previous method by adding a window function, W(q), thatexcludes low rate DL throughput predictions.

6. R(qu)×(Tb×Db′) (See FIGS. 8 and 10, and Equation (19) Above)

This improves method 4 above by replacing the average data rate term,Rb, with an average bit duration term, Db′.

7. R(qu)×((Tb×Db′)×W(qu)) (See FIGS. 8 and 10, and Equation (20) Above)

This extends method 6 by adding a window function, W(q), that excludeslow rate DL throughput predictions.

Methods 4-7 are described in FIGS. 8-10, which are discussed below ingreater detail.

In some instances, method 7 may provide the most accurate prediction ofDL throughput for a specific UE of all the methods listed above.

A generalized form of the DL throughput prediction algorithm is givenby:

Throughput(qu)=R(qu)×F(q)  equation (19)

where F(q) is a weighting function on the basic RSSI to data ratefunction R(q).

F(q) can take various embodiments, such as:

-   -   A constant (the prediction then degenerates to the basic RSSI to        data rate function of method 2),    -   Tb/Rb, as in method 4,    -   (Tb/Rb)×W(qu), as in method 5,    -   Tb×Db′, as in method 6, and    -   Tb×Db′×W(qu), as in method 7.

FIG. 8 illustrates a flowchart 100A in which methods 4 or 5 discussedabove are applied (depending on whether optional window function ofblock 118 is applied). Thus, the flowchart either omits the windowfunction of block 118 (in which case method 4 and equation (11) aboveare applied), or includes the window function of block 118 (in whichcase the method 5 and equation (15) above are applied). Equations (11)and (15) are restated below for convenience.

Method 4: Throughput(qu)˜=R(qu)×(Tb/Rb)  equation (11)

Method 5: Throughput(qu)˜=R(qu)×((Tb/Rb)×W(qu))  equation (15)

FIG. 8 shows how each of the terms of these equations can be determined.Tb will be discussed first. A sum of burst durations is determined overall DL bursts transmitted from the AP 24 to a plurality of UEs 26 in theBSS 24 (e.g., all UEs in the BSS) during a time window (block 102)(“first time window”), which yields Db. A sum is also determined of thenumber of bits in each of those DL bursts sent to the plurality of UEs26 in the BSS 24 (e.g., all of the UEs in the BSS) during the same timewindow (block 104), which yields Bb. An average UE DL throughput amongthe plurality of UEs in the BSS 24 (e.g., all of the UEs in the BSS) iscalculated for the time window (block 106), using equation (7) above,which yields Tb. In some embodiments, the time window that is used inblocks 102, 104 may be on the order of 100 seconds, for example.Although FIG. 8 shows “the plurality of UEs” in blocks 102, 104 asincluding all UEs in the BSS, it is understood that some UEs could beomitted, such that the plurality of UEs for these blocks did not includeall UEs in the BSS.

The term R(qu) will now be discussed (which is the output of block 110).A moving average of signal qualities (e.g., RSSI values) over a firsttime window is calculated (block 108) for a first set of uplinktransmissions which are sent from a particular UE 26 “u” to the AP 24(see input to block 108). The output of block 108 is an average signalquality, qu, for the particular UE “u”. FIG. 8 indicates that the signalqualities may be RSSI values. Thus, in some embodiments, the signalquality is a RSSI value (as RSSI may be an estimate of SNR, rather thanan estimate of absolute signal strength). Optionally, the moving averageof block 108 may be time-weighted.

Based on the calculated moving average, qu, an expected data rate,R(qu), for the particular UE “u” is calculated based on a data ratefunction that maps signal quality values to DL data rates, or acorresponding look-up table. The data rate function may be expressed asa pre-calculated look-up table, for example. The expected data rate(e.g., bit rate) is then utilized as R(qu) for method 4 or 5 above.

The term 1/Rb will now be discussed. Based on a second set up uplinktransmissions, which include 802.11 ACKs and/or block ACKs from aplurality of UEs 26 in the BSS 24 (e.g., all UEs in the BSS), ahistogram of RSSI values, q, over a time window (“second time window”)is determined (block 112), which yields P(q). Although FIG. 8 shows “theplurality of UEs” which provide input to block 112 as including all UEsin the BSS, it is understood that some UEs could be omitted, such thatthe plurality of UEs for these blocks did not include all UEs in theBSS.

An expected value of data rate Rb is then calculated based on P(q) andthe data rate function or look-up table (block 114), which yields theterm 1/Rb. The DL throughput may then be calculated based on equation(11) above, using R(qu), Tb, and Rb. Optionally, window function W(q)(block 118) may be applied to exclude UL transmissions having a signalquality below a predefined threshold from the first set of uplinktransmissions.

FIG. 9 illustrates a method 100B which in which methods 6 or 7 discussedabove are applied (depending on whether the optional window function ofblock 118 is applied). Thus, the flowchart either omits the windowfunction of block 118 (in which case method 6 and equation (19) aboveare applied), or includes the window function of block 118 (in whichcase the method 7 and equation (20) above is applied). Equations (19)and (20) are restated below for convenience.

Method 6: Throughput(qu)˜=R(qu)×Tb×Db′  equation (19)

Method 7: Throughput(qu)˜=R(qu)×Tb×Db′×W(qu)  equation (20)

Blocks 102-112 are the same in FIG. 9 as they are in FIG. 8. However, inFIG. 9 block 120 replaces block 114, such that an expected bit durationDb′ is calculated instead of calculating an expected data rate Rb. Inblock 116′, UE DL throughput is calculated based on equation (19) aboveusing R(qu), Tb, and Db′. Optionally, window function W(q) (block 118)may be applied to exclude UL transmissions having a signal quality belowa predefined threshold from the first set of uplink transmissions.

FIG. 10 illustrates an example method 200 that is a broader formulationof the methods 100A, 100B. The method 200 is implemented by a networknode in relation to a BSS 24 that includes an access point 22 providinga WLAN, and includes a particular UE 26 that is either part of the BSS24, or is being evaluated for admission to the BSS 24. The network nodeimplementing method 100 could be the WLAN AP 22, or the centralcontroller 30, or any control or data forwarding node in the network 20,for example. According to the method 200, the network node predicts(block 202) a DL data rate for DL transmissions from the access point 22to the particular UE 26 based on either signal qualities of a first setof uplink transmissions which are sent from the particular UE 26 to theaccess point 22 (e.g., R(qu)) or a default signal quality value.

The network node weights the predicted DL data rate (block 204) by ametric that accounts for channel conditions of the BSS 24 to determine apredicted DL throughput for DL transmissions from the AP 22 to theparticular UE 26. That metric may be based on Tb/Rb (as in methods 4-5above) or TB×Db′ (as in methods 6-7 above), for example. The networknode then uses the predicted DL throughput for controlling the BSS 24(block 206).

In some embodiments, controlling the BSS (block 206) comprises changinga connectivity of the particular UE with respect to the BSS. This couldinclude admission control, traffic overload control, and/or mobilitycontrol (e.g., load balancing), for example. In some embodiments,controlling the BSS comprises performing performance monitoring of thenetwork (e.g., for longer term changes that are implemented after theparticular UE is no longer connected and/or no longer trying connect tothe BSS).

The metric that accounts for channel conditions of the BSS 24 may bebased on an average UE DL throughput of DL transmissions from the accesspoint 22 to a plurality of respective UEs 26 in the BSS 244 (e.g., Tb).In some such embodiments, the average UE DL throughput among theplurality of UEs in the BSS is calculated based on a duration of each ofa plurality of DL bursts transmitted from the access point to theplurality of UEs 26, and a quantity of bits transmitted in each of theplurality of DL bursts (e.g., as shown in block 106).

In some embodiments of the method 200, the weighting of the predicted DLdata rate by the metric that accounts for channel conditions of the BSSis performed as a function of an additional DL data rate (e.g., Rb)which is based on signal qualities of a second set of uplinktransmissions which are sent from a plurality of UEs in the BSS to theAP 24 (see, e.g., block 114).

In some such embodiments, the weighting of the predicted DL data rate bya metric that accounts for channel conditions of the BSS is performedaccording to equation (11), which is reproduced below.

$\begin{matrix}{{{Throughput}({qu})} = \frac{{R({qu})} \times {Tb}}{Rb}} & {{equation}\mspace{14mu} (11)}\end{matrix}$

According to such embodiments:

-   -   qu is a signal quality of a given uplink transmission, or a set        of uplink transmissions over a time window from the particular        UE “u”;    -   R(qu) is the predicted DL data rate for DL transmissions from        the access point to the particular UE which is based on either        the signal quality of the first set of uplink transmissions or        the default signal quality value;    -   Tb is the average UE DL throughput of DL transmissions from the        access point to a plurality of respective UEs in the BSS; and    -   Rb is the additional DL data rate that is based on a signal        quality of the second set of uplink transmissions; and        In such embodiments, Rb is calculated according to the equation        (14), which is reproduced below.

$\begin{matrix}{{Rb} = \frac{\sum\limits_{q}^{\;}{{P(q)} \times {R(q)}}}{\sum\limits_{q}^{\;}{P(q)}}} & {{equation}\mspace{14mu} (14)}\end{matrix}$

According to such embodiments, P(q) is a histogram of signal qualityvalues of the second set of uplink transmissions, and R(q) is a mappingfunction or look-up table that maps signal quality values to downlinkdata rates.

In some embodiments (e.g., that of FIG. 9), the weighting of thepredicted DL data rate by the metric that accounts for channelconditions of the BSS is performed as a function of an expected DL bitduration (e.g., Db′) which is based on a signal quality of the secondset of uplink transmissions which are sent from a plurality of UEs inthe BSS to the access point.

In some such embodiments, the weighting of the DL data rate by a metricthat accounts for channel conditions of the BSS is performed accordingto equation (19), which is reproduced below:

Throughput(qu)=R(qu)×Tb×Db′  equation (19)

In such embodiments:

-   -   qu is a signal quality of a given uplink transmission, or of a        set of uplink transmissions over a time window from the        particular UE “u”;    -   R(qu) is the predicted DL data rate for DL transmissions from        the access point to the particular UE “u” which is based on        either the signal quality of the first set of uplink        transmissions or the default signal quality value;    -   Tb is the average UE DL throughput of DL transmissions from the        access point to a plurality of respective UEs in the BSS;    -   Db′ is the expected DL bit duration; and        In such embodiments, Db′ is calculated according to the equation        (18), which is reproduced below:

${Db}^{\prime} = \frac{\sum\limits_{q}^{\;}{{P(q)}*\left( {1/{R(q)}} \right)}}{\sum\limits_{q}^{\;}{P(q)}}$

In this equation, P(q) is a histogram of signal quality values of thesecond set of uplink transmissions, and R(q) is a mapping function orlook-up table that maps signal quality values to downlink data rates.

As discussed above, R(q) may be determined based on a mapping functionor look-up table that maps signal quality values to DL data rates.

The “first set of uplink transmissions” discussed above (e.g., the inputto block 108 in FIGS. 8-9), are received from the particular UE over afirst time window, and the second set of uplink transmissions (e.g., theinput to block 112 in FIGS. 8-9) are received from a plurality of UEs inthe BSS (e.g., all UEs in the BSS) over a second time window. In someembodiments, the second time window is longer than the first timewindow. In some embodiments, the only uplink transmissions that areincluded in the second set of uplink transmissions are acknowledgment(ACK) and/or block ACK transmissions sent in response to the DLtransmissions that were used to measure the average UE downlinkthroughput, Tb.

In any of the embodiments discussed above, window function W(q) mayoptionally be applied to exclude uplink transmissions having a signalquality below a predefined threshold from the first set of uplinktransmissions.

In some embodiments, said predicting a DL data rate for DL transmissionsfrom the AP 22 to the particular UE 26 based on a signal quality of thefirst set of uplink transmissions includes calculating a moving averageof signal quality values of the first set of uplink transmissions (block106), and predicting the DL data rate for the particular UE based on thecalculated moving average and a mapping function that maps signalquality values to data rates (block 116).

FIG. 11 is a flowchart 300 illustrating one embodiment of block 102 inFIGS. 8-9, for measuring the sum of burst durations of an individual UEwithin a BSS, Db(u), and then summing the burst durations over all UEsin the BSS to get Db. At block 302, no data is queued for a UE 26, thatwill be referred to as UE “u”. If DL data arrives for the UE “u” (a“Yes” to block 304), then a start time of a burst i for UE “u” isrecorded, denoted Dbstart(u,i) (block 306). Once all DL queues for UE“u” are empty (a “Yes” to block 308), an end time for burst i of UE “u”is recorded, denoted Dbend(u,i) (block 310). A duration of the burst iis calculated based on a difference between Dbend(u,i) and Dbstart(u,i)(block 312). The burst duration, Db(u,i), is added to a running sum ofburst durations for UE u, denoted Db(u) (block 314). The running sum ofburst durations, Db(u), is then added to a running sum of burstdurations for all UEs in the BSS, denoted Db (block 316).

FIG. 12 illustrates an example network node 400 that may be configuredto implement one or more of the techniques described above. The networknode may be an AP 22, may be a WLAN control node 30 (e.g., a WLAN accesscontroller), or any control or data forwarding node in the network 20,for example. The network node 400 includes one or more processingcircuits (shown as “processor” 402), a communication interface 404, anda memory circuit 406. The one or more processing circuits arecommunicatively connected to both the communication interface 404 andthe memory circuit 406.

The one or more processing circuits 402 may include, for example, one ormore microprocessors, microcontrollers, digital signal processors, orthe like, configured with appropriate software and/or firmware to carryout one or more of the techniques discussed above. The memory circuit408 may comprise one or several types of memory such as read-only memory(ROM), random-access memory, cache memory, flash memory devices, opticalstorage devices, etc. The memory circuit 408 includes programinstructions for predicting a UE DL throughput in a BSS according to oneor more of the techniques described herein. The communication interfacemay comprise a WLAN transceiver, for example.

The one or more processing circuits 402 are configured to predict a DLdata rate for DL transmissions from an AP 22 to a particular UE 26 basedon either signal qualities of a first set of uplink transmissions whichare sent from the particular UE 26 to the AP or a default signal qualityvalue. The access point provides a WLAN and is part of the BSS. Theparticular UE 26 is either already part of the basic service set (BSS)24 or is being evaluated for admission to the BSS 24. The one or moreprocessing circuits are further configured to weight the predicted DLthroughput for DL transmissions from the AP to the particular UE in theBSS, and use the predicted DL throughput to control the BSS.

In some embodiments, processing operations are split between the AP 22and a WLAN control entity (e.g., the WLAN control node 30 or any controlor data forwarding node in the network 20). This may be done such thatthe AP 22 performs the processing with the tightest real-timeconstraints, while the WLAN control entity performs the processing withlooser real-time constraints. For example, in one embodiment the AP 22would measure the following items and report them to the WLAN controlentity:

-   -   the burst duration counters, Db(u) and Db,    -   the burst bit counters, Bb(u) and Bb,    -   the RSSI values from all UEs, and then calculate the RSSI        histogram, P(q) (block 112), and    -   the RSSI values from the particular UE, for which we want to        predict DL throughput, and calculation of the average RSSI for        the particular UE (block 108).        The WLAN control entity would then:    -   calculate the expected data rate (e.g., bit rate) R(qu), for the        particular UE (block 110),    -   calculate Rb or Db′ (block 114 or 120),    -   calculate Tb (block 106), and    -   calculate the predicted DL throughput (block 116).        The predicted DL throughput could be used for many possible use        cases, including:    -   admission control of a UE to an AP, possibly with a dependence        on the desired Quality of Service (QoS) of the UE,    -   overload control at an AP, where one wishes to select a UE to        disassociate to limit load,    -   handover control of a UE between frequency bands of the same AP,    -   handover control of a UE between APs, and    -   handover control of a UE between an AP and a cellular network        (either direction).        In other embodiments, the AP 22 may perform the actions        described in FIG. 8 or 9, instead of relying on the WLAN control        entity to do so. The WLAN control entity could be a variety of        different nodes, such as WLAN control node 30, or any control or        data forwarding node in the network 20.

According to another aspect of the present disclosure, a computerprogram product is stored in a non-transitory computer-readable medium(e.g., memory circuit 406) for controlling a BSS (24). The computerprogram product comprises software instructions which, when run byprocessor (402) in network node (400), configures the network node (400)to:

-   -   predict a downlink data rate for downlink transmissions from        access point (22) to a particular UE (26) based on either signal        qualities of a first set of uplink transmissions which are sent        from the particular UE (26) to the access point (22) or a        default signal quality value, wherein the access point (22)        provides a Wireless Local Area Network (WLAN) and is part of the        BSS (24), and wherein the particular UE (26) is either already        part of the BSS (24) or is being evaluated for admission to the        BSS (24);    -   weight the predicted downlink data rate by a metric that        accounts for channel conditions of the BSS (24) to determine a        predicted downlink throughput for downlink transmissions from        the access point (22) to the particular UE (26) in the BSS (24);        and    -   use the predicted downlink throughput to control the BSS (24).        In some embodiments, the computer program product may further        configure the network node to implement any combination of the        techniques discussed above.

UE Specific DL Throughput Prediction While Active

In FIGS. 8-9 above, block 108 describes calculation of a moving averageof signal qualities (e.g., RSSI values) for a UE “u”, to be used as abasis for determining the UE's average signal quality “qu”. If theparticular UE 26 for which a DL throughput estimate is desired isactively moving, the following options for determining “qu” may bedesirable:

(A) Same as method 7 above. If the UE has not received DL trafficrecently, the AP 22 will not have a recent estimate of the UE's UL RSSI.In that case, a default conservative (low) value of RSSI could be useduntil UL RSSI measurements are again available.

(B) Measurement of the individual UE's DL throughput, in a mannersimilar to the average UE DL throughput measurement, but averaging onlyover the data bursts of the individual UE. This would require additionalmemory to store the individual UE measurements of timestamps, Db(u,l),and bit counters, Bb(u,l), plus a small amount of additional processingload to calculate the DL throughput whenever needed, i.e., the ratio ofthe bit counter to the accumulated time. One could switch betweenoptions (A) and (B), so that option (A) is used prior to the UE 26exchanging a significant amount of user plane traffic, while option (B)is used after that. This option (B) will be inaccurate if the UE has notreceived DL traffic recently, but the BSS loading or interferencecondition has changed recently.

Of these, option (A) may be superior, since it is sensitive to changesin BSS loading and interference, and it should also provide moreconsistent predictions when compared with the predictions prior to theUE entering the BSS.

Histogram Counting

In some embodiments, when collecting the histogram, P(q), the AP countsonly the ACK or Block ACK control frames that the AP receives inresponse to DL frame transmissions. In some embodiments, the DL frametransmissions satisfy the following criteria:

-   -   the DL frame type is either data or management, not control,    -   the frame sub type requires an ACK, i.e., do not include frames        that include with a No-ACK sub type, and    -   the destination address is unicast (not broadcast).

The reason for counting ACKs to these specific DL frames is that theyoccur closely in time to (right after) the DL frames that are beingcollected for the DL UE DL throughput estimate, Tb.

In cases where multiple DL data frames are transported in an aggregateMPDU, the Block ACK should be counted multiple times per aggregate MPDUaccording to the number of frames that are acknowledged successfully bythe Block ACK.

RSSI Measurement and Filtering

When measuring the RSSI, qu, for use in the R(q) function, several RSSIvalues should be averaged to improve the estimate. The RSSI valuesshould be close in time to avoid averaging over more than the period ofslow fading, while still averaging over the period of fast fading. Thesetime scales are band specific.

When the UE is attempting to associate with the BSS, several frames areexchanged with the AP before the network makes a decision on whether toadmit the UE. Thus the RSSI measurements of these frames could beaveraged. Consideration can then be given to what sort of weightingfunction should be applied to the historical RSSI samples.

One simple form of weighting is the exponentially weighted movingaverage, which behaves similarly to a single-stage, low-pass infiniteimpulse response (IIR) digital filter when the samples are equallyspaced in time. The averaging function is defined as:

y(n)=a*x(n)+(1−a)*y(n−1)  equation (21)

where:

-   -   y(n) and y(n−1) are the current and previous RSSI estimates        (output),    -   x(n) is the current RSSI measurement (input),    -   a is the filter coefficient that determines how quickly older        inputs are unweighted, and    -   y(0)=x(0).

The filter coefficient, a, should be chosen based on the expected timebetween samples. However, the exchange of MAC frames is fairly erraticin time.

A time dependent, rather than event dependent, weighting function may besuperior. Such a weighting function could be formed in a fashion similarto an FIR digital low-pass filter, as follows:

$\begin{matrix}{{y(t)} = \frac{\sum\limits_{t_{k}}^{\;}{{w\left( {t - t_{k}} \right)}*{x\left( t_{k} \right)}}}{\sum\limits_{t_{k}}^{\;}{w\left( {t - t_{k}} \right)}}} & {{equation}\mspace{14mu} (22)}\end{matrix}$

where:

-   -   y(t) is the time averaged estimate of the RSSI at time t,    -   w(t) is a weighting function,    -   x(t_(k)) is the sample of the RSSI taken at time t_(k), and    -   t_(k) is time bounded to the range t to (t−T)

The weighting function, w(t), should be selected to give higher weightto more recent samples, and less weight to older samples. A suitableweighting function could be a negative exponential:

w(t)=exp(−t/t ₀)  equation (23)

The value t₀ determines the rate of decay of the exponential. Forsimplicity of implementation, w(t) could be discretized in time so thatonly a few values of the weighting function need to be stored.

The coherence time of a Wi-Fi channel is given approximately by:

T=1/(2*B _(Doppler))  equation (24)

where B_(Doppler) is the Doppler spread of the channel.

Doppler shifts occur when either the Wi-Fi station or a reflector on oneor more of the multiple signal propagation paths is moving. The Dopplershift due to a moving station is given by:

F _(Doppler) =f*(v/c)  equation (25)

where:

-   -   f is the carrier frequency,    -   v is the speed of the station relative to the AP, and    -   c is the speed of light.

Thus for a person walking at 1 m/s, the Doppler shifts and approximatecoherence times for the 2.4 GHz and 5 GHz bands would be:

F _(Doppler,2.4)=2.4e9*(1/3e8)=8 Hz

T=1/(2*F _(Doppler))=63 ms

F _(Doppler,5)=5e9*(1/3e8)=17 Hz

T=1/(2*F _(Doppler))=30 ms

Averaging could improve the RSSI estimate in two ways. First, ifmultiple samples are averaged within less than the coherence time of thechannel, the averaging could reduce the error due to thermal noise andother noise sources that are uncorrelated with the channel between theWi-Fi transmitter and receiver, since the channel response is fairlyconstant over that duration. Secondly, if samples are averaged over aperiod much longer than the coherence time, then the averaging willreduce the error due to fast fading, i.e., signal strength changes dueto motion of the stations or reflectors.

In a typical scenario where a UE is attempting to associate with an AP,the sequence of frames that are exchanged before deciding on admittingthe UE occur over a fairly short time on the order of 100 ms. Howeverthere could also be significant delays in responses for some messages,such as the delay in exchanging and processing RADIUS messages with theAAA server during EAP authentication.

Initializing Tb and Db

Prior to transmitting a significant amount of DL data, the estimatedaverage DL throughput, Tb, may be inaccurate. Therefore, it may bedesirable to replace Tb by a configured constant value until a thresholdamount of DL data is measured.

Similarly, it may be desirable to replace the estimated average bitduration, Db, by a configured constant value until a threshold amount ofDL data is measured.

Dual Band Operation

When 2 operating bands are enabled for inter-RAT steering in the Wi-FiAP, e.g., one BSS in one 2.4 GHz channel and a second BSS in one 5 GHzchannel, separate predictions of performance may be used for each BSS.Each BSS will have its own transmit power, propagation conditions, andloading.

Several methods for estimating the DL throughput of a WLAN station havebeen proposed and investigated. For the scenario where a UE is movinginto the BSS coverage area and attempting to associate, the recommendedmethod is to measure the UL RSSI of the initial frames that are receivedat the AP, and calculate a predicted DL throughput for the UE usingequation 20, which is reproduced below:

Throughput(qu)˜=R(qu)×Tb×Db′×W(qu)  equation (20)

where:

-   -   Tb is the average UE DL throughput in the cell over the recent        past (e.g., 1 minute),    -   R(qu) is the predicted DL data rate for a UE “u” based on the UL        RSSI, qu, measured at the AP (determined using a look-up table        or function that maps the measured RSSI, qu, into a DL data        rate),    -   W(q) is an arbitrary window function that can exclude RSSI        measurements below some threshold value, and    -   Db′ is the expected value of the bit duration.

It is understood that in some embodiments, the blocks of the flowchartsabove may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. In FIG. 8, forexample, blocks 106, 110, and 114 may occur sequentially, orsimultaneously.

The present disclosure may, of course, be carried out in other ways thanthose specifically set forth herein without departing from essentialcharacteristics of the present disclosure. The present embodiments areto be considered in all respects as illustrative and not restrictive,and all changes coming within the meaning and equivalency range of theappended claims are intended to be embraced therein.

What is claimed is:
 1. A method implemented by a network node inrelation to a basic service set (BSS) that includes an access pointproviding a Wireless Local Area Network (WLAN), the method comprising:predicting a downlink data rate for downlink transmissions from theaccess point to a particular UE based on either signal qualities of afirst set of uplink transmissions which are sent from the particular UEto the access point or a default signal quality value, wherein theparticular UE is either already part of the BSS or is being evaluatedfor admission to the BSS; weighting the predicted downlink data rate bya metric that accounts for channel conditions of the BSS to determine apredicted downlink throughput for downlink transmissions from the accesspoint to the particular UE; and using the predicted downlink throughputfor controlling the BSS.
 2. The method of claim 1, wherein the metricthat accounts for channel conditions of the BSS is based on an averageUE downlink throughput of downlink transmissions from the access pointto a plurality of respective UEs in the BSS.
 3. The method of claim 2,further comprising calculating the average UE downlink throughput amongthe plurality of UEs in the BSS based on a duration of each of aplurality of downlink bursts transmitted from the access point to theplurality of UEs, and a quantity of bits transmitted in each of theplurality of downlink bursts.
 4. The method of claim 2, wherein theweighting of the predicted downlink data rate by the metric thataccounts for channel conditions of the BSS is performed as a function ofan additional downlink data rate which is based on signal qualities of asecond set of uplink transmissions which are sent from a plurality ofUEs in the BSS to the access point.
 5. The method of claim 4, whereinthe weighting of the predicted downlink data rate by a metric thataccounts for channel conditions of the BSS is performed according to thefollowing equation:${{Throughput}({qu})} = \frac{{R({qu})} \times {Tb}}{Rb}$ where: qu isa signal quality of a given uplink transmission, or of a set of uplinktransmissions over a time window from the particular UE; R(qu) is thepredicted downlink data rate for downlink transmissions from the accesspoint to the particular UE which is based on either the signal qualityof the first set of uplink transmissions or the default signal qualityvalue; Tb is the average UE downlink throughput of downlinktransmissions from the access point to a plurality of respective UEs inthe BSS; and Rb is the additional downlink data rate that is based on asignal quality of the second set of uplink transmissions; and wherein Rbis calculated according to the following equation:${Rb} = \frac{\sum\limits_{q}^{\;}{{P(q)} \times {R(q)}}}{\sum\limits_{q}^{\;}{P(q)}}$where: P(q) is a histogram of signal quality values of the second set ofuplink transmissions; and R(q) is a mapping function or look-up tablethat maps signal quality values to downlink data rates.
 6. The method ofclaim 5, wherein the only uplink transmissions that are included in thesecond set of uplink transmissions are acknowledgment (ACK) and/or blockACK transmissions sent in response to the downlink transmissions thatwere used to measure the average UE downlink throughput, Tb.
 7. Themethod of claim 5, wherein the first set of uplink transmissions arereceived from the particular UE over a first time window, and the secondset of uplink transmissions are received from all UEs in the BSS over asecond time window that is longer than the first time window.
 8. Themethod of claim 2, wherein the weighting of the predicted downlink datarate by the metric that accounts for channel conditions of the BSS isperformed as a function of an expected downlink bit duration which isbased on a signal quality of a second set of uplink transmissions whichare sent from a plurality of UEs in the BSS to the access point.
 9. Themethod of claim 8, wherein the weighting of the downlink data rate by ametric that accounts for channel conditions of the BSS is performedaccording to the following equation:Throughput(qu)=R(qu)×Tb×Db′ where: qu is a signal quality of a givenuplink transmission, or of a set of uplink transmissions over a timewindow from the particular UE; R(qu) is the predicted downlink data ratefor downlink transmissions from the access point to the particular UEwhich is based on either the signal quality of the first set of uplinktransmissions or the default signal quality value; Tb is the average UEdownlink throughput of downlink transmissions from the access point to aplurality of respective UEs in the BSS; and Db′ is the expected downlinkbit duration; and wherein Db′ is calculated according to the followingequation:${Db}^{\prime} = \frac{\sum\limits_{q}^{\;}{{P(q)}*\left( {1/{R(q)}} \right)}}{\sum\limits_{q}^{\;}{P(q)}}$where: P(q) is a histogram of signal quality values of the second set ofuplink transmissions; and R(q) is a mapping function or look-up tablethat maps signal quality values to downlink data rates.
 10. The methodof any claim 9, wherein the only uplink transmissions that are includedin the second set of uplink transmissions are acknowledgment (ACK)and/or block ACK transmissions sent in response to the downlinktransmissions that were used to measure the average UE downlinkthroughput, Tb.
 11. The method of claim 9, wherein the first set ofuplink transmissions are received from the particular UE over a firsttime window, and the second set of uplink transmissions are receivedfrom all UEs in the BSS over a second time window that is longer thanthe first time window.
 12. The method of claim 1, further comprisingapplying a window function to exclude uplink transmissions having asignal quality below a predefined threshold from the first set of uplinktransmissions.
 13. The method of claim 1, wherein said predicting adownlink data rate for downlink transmissions from the access point tothe particular UE based on a signal quality of the first set of uplinktransmissions comprises: calculating a moving average of signal qualityvalues of the first set of uplink transmissions; and predicting thedownlink data rate for the particular UE based on the calculated movingaverage and a mapping function that maps signal quality values to datarates.
 14. The method of claim 1, wherein controlling the BSS compriseschanging a connectivity of the particular UE with respect to the BSS.15. A network node comprising: a communication interface; and one ormore processing circuits communicatively connected to the communicationinterface, and configured to: predict a downlink data rate for downlinktransmissions from an access point to a particular UE based on eithersignal qualities of a first set of uplink transmissions which are sentfrom the particular UE to the access point or a default signal qualityvalue, wherein the access point provides a Wireless Local Area Network(WLAN) and is part of a basic service set (BSS), and wherein theparticular UE is either already part of the BSS or is being evaluatedfor admission to the BSS; weight the predicted downlink data rate by ametric that accounts for channel conditions of the BSS to determine apredicted downlink throughput for downlink transmissions from the accesspoint to the particular UE in the BSS; and use the predicted downlinkthroughput to control the BSS.
 16. The network node of claim 15, whereinthe metric that accounts for channel conditions of the BSS is based onan average UE downlink throughput of downlink transmissions from theaccess point to a plurality of respective UEs in the BSS.
 17. Thenetwork node of claim 16, wherein the one or more processing circuitsare further configured to calculate the average UE downlink throughputamong the plurality of UEs in the BSS based on a duration of each of aplurality of downlink bursts transmitted from the access point to theplurality of UEs, and a quantity of bits transmitted in each of theplurality of downlink bursts.
 18. The network node of claim 16, whereinthe one or more processing circuits are configured to weight thepredicted downlink data rate by the metric that accounts for channelconditions of the BSS as a function of an additional downlink data ratefor the particular UE which is based on a signal quality of a second setof uplink transmissions which are sent from a plurality of UEs in theBSS to the access point.
 19. The network node of claim 18, wherein theone or more processing circuits are configured to weight the predicteddownlink data rate by a metric that accounts for channel conditions ofthe BSS according to the following equation:Throughput(qu)=R(qu)×Tb×Rb where: qu is a signal quality of a givenuplink transmission, or a set of uplink transmissions over a time windowfrom the particular UE “u”; R(qu) is the predicted downlink data ratefor downlink transmissions from the access point to the particular UEwhich is based on either the signal quality of the first set of uplinktransmissions or the default signal quality value; Tb is the average UEdownlink throughput of downlink transmissions from the access point to aplurality of respective UEs in the BSS; and Rb is the additionaldownlink data rate for the particular UE that is based on a signalquality of the second set of uplink transmissions; and wherein Rb iscalculated according to the following equation:${Rb} = \frac{\sum\limits_{q}^{\;}{P(q)}}{\sum\limits_{q}^{\;}{{P(q)} \times {R(q)}}}$where: P(q) is a histogram of signal quality values of the second set ofuplink transmissions; and R(q) is a mapping function or look-up tablethat maps signal quality values to downlink data rates.
 20. The networknode of claim 19, wherein the only uplink transmissions that areincluded in the second set of uplink transmissions are acknowledgment(ACK) and/or block ACK transmissions sent in response to the downlinktransmissions that were used to measure the average UE downlinkthroughput, Tb.
 21. The network node of claim 19, wherein the first setof uplink transmissions are received over a first time window, and thesecond set of uplink transmissions are received from all UEs in the BSSover a second time window.
 22. The network node of claim 15, wherein theone or more processing circuits are configured to weight the predicteddownlink data rate by the metric that accounts for channel conditions ofthe BSS as a function of an expected downlink bit duration for theparticular UE which is based on a signal quality of a second set ofuplink transmissions which are sent from a plurality of UEs in the BSSto the access point.
 23. The network node of claim 22, wherein the oneor more processing circuits are configured to weight the downlink datarate by a metric that accounts for channel conditions of the BSSaccording to the following equation:Throughput(qu)=R(qu)×Tb×Db′ where: qu is a signal quality of a givenuplink transmission, or a set of uplink transmissions over a time windowfrom the particular UE “u”; R(qu) is the predicted downlink data ratefor downlink transmissions from the access point to the particular UEwhich is based on either the signal quality of the first set of uplinktransmissions or the default signal quality value; Tb is the average UEdownlink throughput of downlink transmissions from the access point to aplurality of respective UEs in the BSS; and Db′ is the expected downlinkbit duration for the particular UE; and wherein Db′ is calculatedaccording to the following equation:${Db}^{\prime} = \frac{\sum\limits_{q}^{\;}{{P(q)}*\left( {1/{R(q)}} \right)}}{\sum\limits_{q}^{\;}{P(q)}}$where: P(q) is a histogram of signal quality values of the second set ofuplink transmissions; and R(q) is a mapping function or look-up tablethat maps signal quality values to downlink data rates.
 24. The networknode of claim 23, wherein the only uplink transmissions that areincluded in the second set of uplink transmissions are acknowledgment(ACK) and/or block ACK transmissions sent in response to the downlinktransmissions that were used to measure the average UE downlinkthroughput, Tb.
 25. The network node of claim 23, wherein the first setof uplink transmissions are received over a first time window, and thesecond set of uplink transmissions are received from all UEs in the BSSover a second time window.
 26. The network node of claim 15, wherein theone or more processing circuits are further configured to apply a windowfunction to exclude uplink transmissions having a signal quality below apredefined threshold from the first set of uplink transmissions.
 27. Thenetwork node of claim 15, wherein to predict a downlink data rate fordownlink transmissions from the access point to the particular UE basedon a signal quality of the first set of uplink transmissions, the one ormore processing circuits are configured to: calculate a moving averageof signal quality values of the first set of uplink transmissions; andpredict the downlink data rate for the particular UE based on thecalculated moving average and a mapping function or lookup table thatmaps signal quality values to data rates.
 28. The network node of claim15, wherein to control the BSS, the one or more processing circuits areconfigured to change a connectivity of the particular UE with respect tothe BSS.
 29. A computer program product stored in a non-transitorycomputer-readable medium for controlling a basic service set (BSS), thecomputer program product comprising software instructions which, whenrun by a processor in a network node, configures the network node to:predict a downlink data rate for downlink transmissions from an accesspoint to a particular UE based on either signal qualities of a first setof uplink transmissions which are sent from the particular UE to theaccess point or a default signal quality value, wherein the access pointprovides a Wireless Local Area Network (WLAN) and is part of the BSS,and wherein the particular UE is either already part of the BSS or isbeing evaluated for admission to the BSS; weight the predicted downlinkdata rate by a metric that accounts for channel conditions of the BSS todetermine a predicted downlink throughput for downlink transmissionsfrom the access point to the particular UE in the BSS; and use thepredicted downlink throughput to control the BSS.